检索结果-内蒙古大学图书馆

Rotation,Translation and Scale Invariant Sign Word Recognition Using Deep Learning

computer Systems science & engineering 2023年第3期44卷 2521-2536页

作者： Abu Saleh Musa Miah Jungpil Shin Md.Al Mehedi Hasan Md Abdur Rahim Yuichi Okuyama School of Computer Science and Engineering The University of AizuAizuwakamatsuFukushima965-8580Japan Department of Computer Science and Engineering Pabna University of Science and TechnologyPabnaBangladesh

Communication between people with disabilities and people who do not understand sign language is a growing social need and can be a tedious *** of the main functions of sign language is to communicate with each other through hand *** of hand gestures has become an important challenge for the recognition of sign *** are many existing models that can produce a good accuracy,but if the model test with rotated or translated images,they may face some difficulties to make good performance *** resolve these challenges of hand gesture recognition,we proposed a Rotation,Translation and Scale-invariant sign word recognition system using a convolu-tional neural network(CNN).We have followed three steps in our work:rotated,translated and scaled(RTS)version dataset generation,gesture segmentation,and sign word ***,we have enlarged a benchmark dataset of 20 sign words by making different amounts of Rotation,Translation and Scale of the ori-ginal images to create the RTS version *** we have applied the gesture segmentation *** segmentation consists of three levels,i)Otsu Thresholding with YCbCr,ii)Morphological analysis:dilation through opening morphology and iii)Watershed ***,our designed CNN model has been trained to classify the hand gesture as well as the sign *** model has been evaluated using the twenty sign word dataset,five sign word dataset and the RTS version of these *** achieved 99.30%accuracy from the twenty sign word dataset evaluation,99.10%accuracy from the RTS version of the twenty sign word evolution,100%accuracy from thefive sign word dataset evaluation,and 98.00%accuracy from the RTS versionfive sign word dataset ***,the influence of our model exists in competitive results with state-of-the-art methods in sign word recognition.

关键词： Sign word recognition convolution neural network(cnn) rotation translation and scaling(rts) otsu segmentation

来源：评论

学校读者我要写书评

暂无评论

Multi-feature Fusion Deep Network for Skin Disease Diagnosis

引用

Multimedia Tools and Applications 2025年第1期84卷 419-444页

作者： Gairola, Ajay Krishan Kumar, Vidit Sahoo, Ashok Kumar Diwakar, Manoj Singh, Prabhishek Garg, Deepak Department of Computer Science and Engineering Graphic Era Hill University Dehradun India Department of Computer Science and Engineering Graphic Era deemed to be university Dehradun India School of Computer Science Engineering and Technology Bennett University Greater Noida India School of Computer Science and Artificial Intelligence SR University Warangal Telangana Warangal506371 India

The skin acts as an important barrier between the body and the external environment, playing a vital role as an organ. The application of deep learning in the medical field to solve various health problems has generated increasing interest. The objective of this research is to create a new model using a deep network with multi-feature fusion that enables accurate identification of various skin diseases. This paper proposes a fully fused network (FFN), which includes an improved single block (ISB) and an improved fusion block (IFB) to achieve optimal performance. To implement this, a convolutional neural network-based model for multi-class recognition of skin images has been developed, in which ISB is used to segment diseases in skin images. The IFB module is designed to enhance the effectiveness of the fused network. The model is built and evaluated using our proprietary dataset (Skin_disease_v1) along with publicly available datasets, namely ISIC2016, ISIC2017, and HAM10000. The highest accuracy achieved in experiments was 86% for ISB, 90% for IFB, and 92% for FFN using HAM10000 (for ResNet101V2), HAM10000 (for Resnet50 + ResNet101V2), and HAM10000 (for Resnet50 + ResNet101V2). This shows an accuracy improvement of 9.2%, 13.2%, and 15.2% compared to the state-of-the-art ISB, IFB, and FFN approaches, respectively. Our network outperforms existing networks in terms of performance. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Deep learning

来源：评论

学校读者我要写书评

暂无评论

Damaged apple detection with a hybrid YOLOv3 algorithm

引用

Information Processing in Agriculture 2024年第2期11卷 163-171页

作者： Meng Zhang Huazhao Liang Zhongju Wang Long Wang Chao Huang Xiong Luo Schoolof Computer and Communication Engineering University of Science and Technoogy BejingBejing 1083China lShunde Innovation SchoolUniversity of Science and Technology BeijingFoshan 528399China

This paper proposes an improved You Only Look Once(YOLOv3)algorithm for automatically detecting damaged apples to promote the automation of the fruit processing *** the proposed method,a clustering method based on Rao-1 algorithm is introduced to optimize anchor box *** clustering method uses the intersection over the union to form the objective function and the most representative anchor boxes are generated for normal and damaged apple *** verify the feasibility and effectiveness of the proposed method,real apple images collected from the Internet are *** with the generic YOLOv3 and Fast Region-based Convolutional Neural Network(Fast R-CNN)algorithms,the proposed method yields the highest mean average precision value for the test ***,it is practical to apply the proposed method for intelligent apple detection and classification tasks.

关键词： Rao algorithm Apple detection Clustering Smart agriculture

来源：评论

学校读者我要写书评

暂无评论

A Code Search Method Incorporating Code Annotations 19th

A Code Search Method Incorporating Code Annotations

引用

19th EAI International Conference on Collaborative Computing: Networking, Applications and Worksharing, CollaborateCom 2023

作者： Li, Qi Liu, Jianxun Zhang, Xiangping School of Computer Science and Engineering Hunan University of Science and Technology Hunan Xiangtan411201 China Hunan Key Lab for Services Computing and Novel Software Technology Hunan University of Science and Technology Hunan Xiangtan411201 China

ISBN: (纸本)9783031545207

Code search is a technique for users to retrieve code snippets from the Code base using natural language, which is dedicated to retrieve the target code accurately and quickly to improve the efficiency of software development. The deep learning based code search technique greatly improves the accuracy of search by learning the relationship between code and query statements. Since it relies on the extracted code features, acquiring more code features is the key to quickly improve the search performance. However, most of the previous works have not taken code annotations into consideration. In this paper, we take code annotations as code features and apply them to code search, which is named ICA-CS (Code Search that Incorporates Code Annotations). In the method, firstly, the code features are embedded to get the corresponding vector representation. It is then processed by bidirectional LSTM (Long Short-Term Memory) network or multi-head attention respectively, followed by features fusion. And finally, the model is trained by joint embedding and using the minimised ranking loss function. As the experimental results show, on the evaluation metric MRR (mean reciprocal rank) compared to the state-of-the-art models DeepCS, SAN-CS, CARLCS-CNN and SelfAtt, the proposed model improves 48.96%, 17.11%, 41.01% and 13.07%, respectively. © ICST Institute for computer sciences, Social Informatics and Telecommunications engineering 2024.

关键词： Long short-term memory

来源：评论

学校读者我要写书评

暂无评论

Danmaku-Based Automatic Analysis of Real-Time Online Learning Engagement

International Journal of Interactive Mobile Technologies

引用

International Journal of Interactive Mobile Technologies 2024年第8期18卷 127-139页

作者： Zeng, Linzhou Tan, Zhibang Ke, Yougang Xia, Lingling School of Information Science and Engineering Hunan Institute of Science and Technology Yueyang China Academic Affairs Office Hunan Institute of Science and Technology Yueyang China Faculty of Education University of Malaya Kuala Lumpur Malaysia

In recent years, there has been a rapid growth of online learning in higher education. Apart from professional online course platforms, many online video sharing websites have also provided online learning opportunities for college students. One of the most popular websites among college students in China is Bilibili, a Shanghai-based Chinese video sharing website known for its danmaku commenting system. This system enables users to post scrolling comments synchronized with the video timeline while the video is playing. Which attracts young students due to the lively user interaction. As a result, an increasing number of Chinese students are utilizing online courses on Bilibili as a supplementary learning resource alongside traditional classroom learning. Despite its popularity, online learning faces the challenge of students’ lack of participation more than traditional face-to-face learning does. To understand their learning involvement, we propose a novel danmaku-based automatic analysis model that extracts three dimensions of online learning engagement using the Text Mind software. This model enables us to understand the students’ learning patterns both as clusters and as individuals. Based on the model results, we present corresponding intervention strategies for different types of students based on their individual engagement characteristics. © 2024 by the authors of this article.

关键词： Students

来源：评论

学校读者我要写书评

暂无评论

A Cover-Independent Deep Image Hiding Method Based on Domain Attention Mechanism

引用

computers, Materials & Continua 2024年第3期78卷 3001-3019页

作者： Nannan Wu Xianyi Chen James Msughter Adeke Junjie Zhao School of Computer and Software Nanjing University of Information Science and TechnologyNanjing210044China School of Electronics and Information Engineering Nanjing University of Information Science and TechnologyNanjing210044China

Recently,deep image-hiding techniques have attracted considerable attention in covert communication and high-capacity information ***,these approaches have some *** example,a cover image lacks self-adaptability,information leakage,or weak *** address these issues,this study proposes a universal and adaptable image-hiding ***,a domain attention mechanism is designed by combining the Atrous convolution,which makes better use of the relationship between the secret image domain and the cover image ***,to improve perceived human similarity,perceptual loss is incorporated into the training *** experimental results are promising,with the proposed method achieving an average pixel discrepancy(APD)of 1.83 and a peak signal-to-noise ratio(PSNR)value of 40.72 dB between the cover and stego images,indicative of its high-quality ***,the structural similarity index measure(SSIM)reaches 0.985 while the learned perceptual image patch similarity(LPIPS)remarkably registers at ***,self-testing and cross-experiments demonstrate the model’s adaptability and generalization in unknown hidden spaces,making it suitable for diverse computer vision tasks.

关键词： Deep image hiding attention mechanism privacy protection data security visual quality

来源：评论

学校读者我要写书评

暂无评论

Research on Network Traffic Classification Based on Graph Neural Network

IAENG International Journal of Computer Science

引用

IAENG International Journal of computer science 2024年第12期51卷 2043-2050页

作者： Han, Yue Dai, Hong University of Science and Technology Liaoning Liaoning Anshan114051 China School of Computer Science and Software Engineering University of Science and Technology Liaoning Liaoning Anshan China

Network traffic classification is a critical concern in network security and management, essential for accurately differentiating among various network applications, optimizing service quality, and improving user experience. The exponential increase in worldwide Internet users and network traffic is continuously augmenting the diversity and complexity of network applications, rendering the Internet environment increasingly intricate and dynamic. Conventional machine learning techniques possess restricted processing abilities for network traffic attributes and struggle to address the progressively intricate traffic classification tasks in contemporary networks. In recent years, the swift advancement of deep learning technologies, particularly Graph Neural Networks (GNN), has yielded significant improvements in network traffic classification. GNN can capture the structured information among network nodes and extract the latent features of network traffic. Nonetheless, current network traffic classification models continue to exhibit deficiencies in the thoroughness of feature extraction. To tackle the problem, this research proposes a method for constructing traffic graphs utilizing numerical similarity and byte distance proximity by exploring the latent correlations among bytes, and it constructs a model, SDA-GNN, based on Graph Isomorphic Networks (GIN) for the categorization of network traffic. In particular, the Dynamic Time Warping (DTW) distance is employed to evaluate the disparity in byte distributions, a channel attention mechanism is utilized to extract additional features, and a Long Short-Term Memory Network (LSTM) enhances the stability of the training process by extracting sequence characteristics. Experimental findings on two actual datasets indicate that the SDA-GNN model surpasses other baseline techniques across multiple assessment parameters in the network traffic classification task, achieving classification accuracy enhancements of 2.19% and 1.49%

关键词： Long short-term memory

来源：评论

学校读者我要写书评

暂无评论

Research on Network Security Game Based on System Dynamics 24

Research on Network Security Game Based on System Dynamics

引用

2nd International Conference on Artificial Intelligence, Systems and Network Security, AISNS 2024

作者： Zeng, Qiufen Pei, Tingrui School of Mechanical Engineering and Mechanics Xiangtan University Hunan Xiangtan China School of Information Science and Engineering Hunan Institute of Engineering Hunan Xiangtan China College of Information Science and Technology Jinan University Guangdong Zhuhai China School of Computer Science School of Cyberspace Science Xiangtan University Hunan Xiangtan China

ISBN: (纸本)9798400711237

Network security game was usually considered as attack-defense confrontation;however, we need to think out of the box because any network entity may get a chance to launch an attack. The problem of network security which was described as the game between regulatory authority and network entity needs to be analyzed from the perspective of macro management. The model of system dynamic which was combined with traditional game theory was established. Several strategies which were most commonly used were verified and evaluated through simulation model. In order to weaken the instability of network security game, selective punishment strategy was proposed and the simulation result showed that not only in astringency but also in network security maintenance the proposed strategy can achieve good results. © 2024 Copyright held by the owner/author(s).

关键词： System theory

来源：评论

学校读者我要写书评

暂无评论

Central Attention Mechanism for Convolutional Neural Networks

IAENG International Journal of Computer Science

引用

IAENG International Journal of computer science 2024年第10期51卷 1642-1648页

作者： Geng, Y.X. Wang, L. Wang, Z.Y. Wang, Y.G. School of Computer Science and Software Engineering University of Science and Technology Liaoning Anshan114051 China School of Computer Science and Software Engineering University of Science and Technology Liaoning Anshan114051 China Automation Design Institute Metallurgical Engineering Technology Co. Ltd. Dalian116000 China

Model performance has been significantly enhanced by channel attention. The average pooling procedure creates skewness, lowering the performance of the network architecture. In the channel attention approach, average pooling is used to collect feature information to provide representative values. By leveraging the central limit theorem, we hypothesize that the strip-shaped average pooling operation will generate a one-dimensional tensor by considering the spatial position information of the feature map. The resulting tensor, obtained through average pooling, serves as the representative value for the features, mitigating skewness during the process. By incorporating the concept of the central limit theorem into the channel attention operation process, this study introduces a novel attention mechanism known as the"Central Attention Mechanism (CAM)." Instead of directly using average pooling to generate channel representative values, the central attention approach employs star-stripe average pooling to normalize multiple feature representative values into a single representative value. In this way, strip-shaped average pooling can be utilized to collect data and generate a one-dimensional tensor, while star-stripe average pooling can provide feature representative values based on different spatial directions. To generate channel attention for the complementary input features, the activation of the feature representation value is performed for each channel. Our attention approach is flexible and can be seamlessly incorporated into various traditional network structures. Through rigorous testing, we demonstrate the effectiveness of our attention strategy, which can be applied to a wide range of computer vision applications and outperforms previous attention techniques. © (2024), (International Association of Engineers). All rights reserved.

关键词： Tensors

来源：评论

学校读者我要写书评

暂无评论

A novel method using matrix coding on-chain and sharing multimedia data for improved usability and reliability

引用

Multimedia Tools and Applications 2024年第40期83卷 87727-87748页

作者： Yang, Lizhu Qin, Yong School of Computer Science and Engineering Guangzhou Institute of Science and Technology Guangzhou China School of Cyberspace Security Dongguan University of Technology Dongguan China

Blockchain technology has the characteristics of non-tampering and forgery, traceability, and so on, which have good application advantages for the storage of multimedia data. So we propose a novel method using matrix coding on-chain and sharing multimedia data for improved usability and reliability. Based on matrix code, we provide the block matrix coding-based on-chain storing method and the block invertible matrix decoding-based sharing method. The method progressively converts the block to create linearly coupled coded chunks, so the blockchain only sends the chunk sets that have been encoded. Each node also maintains just the ledger relevant to its own operations, which lessens the node’s storage burden. Meanwhile, we make several replications of the chunk set by adding a replication factor to increase the feasibility of chunks. Only when all of the target nodes fail is decoding reconstruction necessary, which further enhances the read performance of the blockchain. Many experimental tests are conducted to evaluate the performance based on various parameters such as time overhead, storage overhead, compression factor, failure factor, and so on. According to theoretical analysis and experimental verification, the method offers good read performance with a high recovery success rate and minimal storage occupation, while guaranteeing the availability and dependability of the block data. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Blockchain

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：